With development of photoelectric acquisition technologies and increasing requirements for high-definition digital videos, an amount of video data is becoming large. Due to limited heterogeneous transmission bandwidths and diversified video applications, higher requirements are continuously imposed on video coding efficiency. Development of a high efficiency video coding (HEVC) standard is initiated according to the requirements
A basic principle of video compression coding is to use correlation between a space domain, a time domain, and a code word to remove redundancy as much as possible. Currently, a prevalent practice is to use a block-based hybrid video coding framework to implement video compression coding by performing steps of prediction (including intra-frame prediction and inter-frame prediction), transform, quantization, entropy coding, and the like. This coding framework shows high viability, and therefore, HEVC still uses this block-based hybrid video coding framework.
In various video coding/decoding solutions, motion estimation or motion compensation is a key technology that affects coding decoding efficiency. In various conventional video coding/decoding solutions, it is assumed that motion of an object is always translational motion, and that motion of all parts of the entire object is the same. Basically, all conventional motion estimation or motion compensation algorithms are block motion compensation algorithms that are established based on a translational motion model (translational motion model). However, motion in the real world is diversified, and irregular motion such as scaling up/down, rotation, or parabolic motion is ubiquitous. Since the ninth decade of the last century, video coding experts have realized universality of irregular motion, and wished to introduce an irregular motion model (for example, an affine motion model) to improve video coding efficiency. However, computational complexity of conventional picture prediction performed based on the affine motion model is usually quite high.